Emotion recognition or understanding the state of the user is important and beneficial for many applications; including games, man-machine interface, virtual agents, etc. For example, if emotional state of the user or game player is known, game/machine can dynamically adapt accordingly (i.e. in a simplistic case, game can become harder/easier). In addition, if there is voice recognition in the system; the emotion of the user can be used to adapt the models or to select appropriate models (acoustic and language models) dynamically to improve voice recognition performance.
Knowledge of user's emotion can be useful for many applications including call centers, virtual agents, and other natural user interfaces. Games can also use emotions as part of game input. For example some game applications can be as follows: whoever stays cool/calm under stress can get more points in the game. This can be used for educational games for kids (i.e. training for tests, performing under stress, reading/spelling tests etc.). Similarly, call centers can use the caller's emotion to decide what to do next. Intelligent man-machine interface can benefit from emotion information; i.e. machine can dynamically adapt based on a user's emotional state; i.e. knowing whether the user is happy, frustrated etc.
There can be even more applications that can benefit from emotion recognition such as training tools/programs for professionals: training medical doctors, training soldiers, training customer support staff, etc. This method can be used for both character analysis and user profile generation.
It is within this context that aspects of the present disclosure arise.